Wind turbine generator set bearing fault feature extraction method based on vibration data

A wind turbine and fault feature technology, applied in the field of wind turbine bearing fault feature extraction based on vibration data, can solve problems such as powerlessness, difficulty in detecting fault feature information, and inability to judge fault types

Inactive Publication Date: 2016-06-22
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

[0004] As the wind power generation system becomes more and more complex and contains more and more components, the frequency components of its vibration signals are very complex, and it is difficult to detect its fault characteristic information
Traditional frequency do

Method used

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  • Wind turbine generator set bearing fault feature extraction method based on vibration data
  • Wind turbine generator set bearing fault feature extraction method based on vibration data
  • Wind turbine generator set bearing fault feature extraction method based on vibration data

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Embodiment Construction

[0066] See attached figure 1 , 2 As shown, the steps of a method for extracting fault features of wind turbine bearings based on vibration data in the present invention are:

[0067] (1) Use the JADE algorithm to perform blind source separation on the observed signal to obtain the source signal

[0068] Blind source separation refers to the process of separating or estimating the source signal from the observed signal according to the statistical characteristics of the source signal when the source signal and the transmission channel are unknown; the observed signal comes from the output of a set of sensors, each sensor A set of mixtures receiving multiple original signals can be modeled as:

[0069]

[0070] In the formula, yes observation signal, is the source signal vector; similarly, for The mixed signal vector of , for The noise vector, m represents the number of rows of the vector, then order mixing matrix, and is a multiplicative relationship;...

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Abstract

The invention relates to a wind turbine generator set bearing fault feature extraction method based on vibration data. The method includes the steps of: 1. using a JADE algorithm to perform blind source separation on an observation signal, so as to obtain a source signal; 2. calculating kurtosis and negentropy of the source signal; 3. calculating a singular value of a source signal envelope matrix; and 4. utilizing a local linear embedding method to extract fault features. The wind turbine generator set bearing fault feature extraction method based on vibration data combines the blind source separation with the local linear embedding method, and is particularly suitable for rotary mechanical equipment such as a bearing; and the method can effectively eliminate noise mixed in a bearing vibration signal process, and separate a fault source signal, thereby providing more accurate information for fault feature extraction.

Description

technical field [0001] The invention relates to the technical field of wind power generation systems, in particular to a method for extracting fault features of wind turbine bearing faults based on vibration data. Background technique [0002] Because most of the wind farms are located in areas with complex and harsh environments, they are often affected by extreme weather. As the accumulative running time of the unit increases, the components of the unit continue to age and are prone to failure. The main shaft, yaw, pitch, generator, gearbox and many other parts of wind turbines are equipped with bearings, and bearing failures account for a high proportion of unit failures. In order to reduce the downtime of wind turbines and reduce the maintenance costs of the wind turbines, it is necessary to monitor the condition of important bearing components of wind turbines. At present, vibration signal analysis technology is one of the main means of state monitoring and fault diag...

Claims

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Application Information

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IPC IPC(8): G01M13/04
Inventor 赵洪山李浪
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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